Image saliency detection method for safe operation of substation

Pengfei Sheng, Qi Yin, Y. Shen
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Abstract

Aiming at the problem that methods based on background templates in existing substation scenes cannot completely detect salient targets with similar characteristics in some areas and the background, a salient target integrity detection method based on contour detection is proposed. First, extract the initial contours of the input image, use the proposed virtual connection-based contour processing scheme to merge adjacent contours and remove isolated contours, and use the designed shortest path-based closed-loop search scheme to merge the contours with longer distances to obtain Saliency map for contour detection. Then, an adaptive threshold segmentation algorithm is used to process the saliency map based on background template suppression to obtain the binarized saliency map and the salient pixels. By removing the complete area of the contour with the proportion of salient pixels smaller than the specified threshold, an optimized saliency map based on contour detection is obtained. Finally, merge it with the binarized saliency map to obtain a complete saliency map. The results prove that the method in this paper can obtain a better saliency map for images where the saliency target is located at any position.
变电站安全运行的图像显著性检测方法
针对现有变电站场景中基于背景模板的方法无法完全检测出某些区域与背景特征相似的显著目标的问题,提出了一种基于轮廓检测的显著目标完整性检测方法。首先,提取输入图像的初始轮廓,使用提出的基于虚拟连接的轮廓处理方案合并相邻轮廓并去除孤立轮廓,并使用设计的基于最短路径的闭环搜索方案合并距离较长的轮廓,获得Saliency map进行轮廓检测。然后,采用基于背景模板抑制的自适应阈值分割算法对显著性图进行处理,得到二值化后的显著性图和显著像素;通过去除显著像素比例小于指定阈值的轮廓的完整区域,得到基于轮廓检测的优化显著性图。最后,将其与二值化后的显著性图合并,得到一个完整的显著性图。结果表明,对于显著性目标位于任意位置的图像,本文方法都能得到较好的显著性图。
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